Conclusion: Compared with Faster RCNN, SSD, YOLOv3, YOLOv4 and YOLOv5s, the test algorithm has higher recognition accuracy, more lightweight model and better recognition effect for coffee defective beans.张成尧张艳诚张宇乾赵玉清Food & Machinery...
In contrast, after reviewing the findings of the same study48, it was observed that the YOLOv3, faster R-CNN, and faster R-CNN + FPN appeared to require less training time48. The comparison of the outcomes of different tea leaf disease detection algorithms against the results obtained ...
In contrast, after reviewing the findings of the same s tudy48, it was observed that the YOLOv3, faster R-CNN, and faster R-CNN + FPN appeared to require less training time48. The comparison of the outcomes of different tea leaf disease detection algorithms against the results...
Since Faster-RCNN loses the underlying information during continuous sampling, it is difficult to detect small defects on the end face of the steel coil. It is proposed to use multi-scale feature fusion to improve the optimization. At the same time, due to the poor performance of the Faster...
Additionally, YOLOv5s-P6SE outperforms Faster R-CNN, SSD, the PCB defect detection model YOLOv4-MN3, and the DETR model RT-DETR-L in terms of both mAP and model size. It also excels in balancing mAP and model size compared to YOLOv8s.梁泰然...
The comparison between Mask R-CNN and YOLOv8 highlights that the Mask R-CNN performs better in defect detection (i.e., Missing Holes and Shorts). In particular, for the missing hole defects, for example, the mAP50-95 is 0.798 for Mask R-CNN and 0.261 for YOLOv8. Instead, for the ...
According to the experimental results, In the detection of red jujube, YOLOv5s, YOLOv4-tiny, and Faster R-CNN all miss the detection, which leads to a decrease in the number of red jujubes. YOLOv3-tiny, SSD, and Faster R-CNN all have error recognition, which leads to the increase in...
According to the experimental results, In the detection of red jujube, YOLOv5s, YOLOv4-tiny, and Faster R-CNN all miss the detection, which leads to a decrease in the number of red jujubes. YOLOv3-tiny, SSD, and Faster R-CNN all have error recognition, which leads to the increase in...
positioning them as one of the few authors to explore the latest versions of YOLO. Their study compared the performance of Faster-RCNN, VFNet, Detr, and YOLO (v3, v5, and v7) with YOLOv9, where YOLOv9 achieved superior speed and accuracy, reaching a nearly 95% defect detection accuracy...